Cost, Price, and The Space Between
I sing my heart out to the wide open spaces
This month, we’ll study the difference between cost and price, why it matters, and how knowing how both behave in tandem is the key to success.
I know from our analytics that most of you are in the business of working out costs and prices. For many, it is hard to separate the two, especially if you work in or with the government. Today’s analysis directs itself to commercial operations. In a future newsletter, we’ll look at how to adapt this framework to the public sector.
All too often in business, someone comes up with a seemingly great idea and gets fellow workers excited about it. It gets pushed into production. Producers then wait to see what the market will bear for it, often falling short of projections.
What if you could change the paradigm?
Suppose you could see market openings and limits and test sample specifications and sales targets before you commit resources to a configuration. That would improve your chances of success.
You’ll have to work to enable this vision, but you will find it worthwhile.
When we at Hypernomics look at a market, we begin with Demand. As shown below as the red plane, that means finding the ordered pairs for Quantity and Price. We create a series of price bins (either equally spaced or binned by geometric or Fibonacci methods) and determine the ordered pairs (as the purple hexagons) representing each bin’s average price and total Quantity. Then we run a regression curve through them, which represents Aggregate Market Demand.
To the left of that curve, we find the Demand Frontier, a regression through the outermost points on the Demand Plane. This curve shows the limit of the products this market can absorb over time. As markets mature, the Aggregate Market Demand and Demand Frontier slopes often approximate one another.
If we examine the points closely, we’ll notice a price gap. Using its midpoint, we would find the 1) Quantity limit the market will support at that price (the vertical red line coming down from the Demand Frontier) and our 2) Target Price (the horizontal red line originating from the Demand Frontier).
To support that price, we’ll need to offer our customers something they like, here as Features A and B, which show up as the green Value Space at left, with the target Price as the horizontal red plane. We’ll need to figure out the Value Surface that the combinations of Features A and B command (the points for which we excluded from this view, for clarity). As seen on the left, there are Cost Surfaces for one or 500 units below the Value Surface. If we further bound our potential offering with Constraints (the vertical orange planes), we now have a region restricted on all sides. Conceptually, this expanse is not different than a like delimited region, such as your head.
Now, if you suspected that you had a deviated septum, your ear, nose, and throat doctor might order a CT scan, in which the doctor would develop section cut views of your head.
We can do the same thing in markets, using Financial CAT scans. Thus, after carefully setting up a 4D arrangement and taking cuts in both the Sections A and B directions, we can predict the 1) maximum Quantity Sold (reducing the 4D problem to one in 3D). Then we selected 2) the Price (dropping the remainder of undetermined dimensions to 2), 3) Feature A (the distance of the black plane from the origin, reducing the problem to 1 dimension), and 4) Feature B (the Vertical Profit Line, the final dimension). The per-unit profit line on the left times the number of units on the Demand Plane gives the projected profit.
In the process, we reduced a 4D problem to a single objective of maximum potential profit.
To complete the analysis, we’d examine all open price points and all viable combinations of the Features considering risk as well, searching for the best potential configuration.
Watch this video to see the analytical steps in action: